Group structure estimation for panel data -- a general approach

Consider a panel data setting where repeated observations on individuals are available. Often it is reasonable to assume that there exist groups of individuals that share similar effects of observed characteristics, but the grouping is typically unknown in advance. We propose a novel approach to estimate such unobserved groupings for general panel data models. Our method explicitly accounts for the uncertainty in individual parameter estimates and remains computationally feasible with a large number of individuals and/or repeated measurements on each individual. The developed ideas can be applied even when individual-level data are not available and only parameter estimates together with some quantification of uncertainty are given to the researcher.